Day 19-ML System Design & MLOps | MLflow, FastAPI, Docker, CI/CD, Monitoring & Deployment

🚀 Day 19 of the Complete Machine Learning Roadmap

Today we move beyond model training and learn how real companies deploy, monitor, scale and maintain Machine Learning systems in production.

This video covers everything required for modern ML Engineering and MLOps roles.

📚 Topics Covered

✅ Research vs Production Gap
✅ What is MLOps?
✅ End-to-End ML Lifecycle
✅ Experiment Tracking with MLflow
✅ Model Registry & Versioning
✅ FastAPI Model Deployment
✅ REST API Design for ML Models
✅ Docker Containerization
✅ CI/CD for Machine Learning
✅ Automated Retraining Pipelines
✅ Data Drift & Concept Drift Monitoring
✅ Evidently AI Monitoring
✅ A/B Testing ML Models
✅ Feature Stores (Feast)
✅ ML System Design Interview Framework
✅ Production ML Architecture
✅ MLOps Tool Ecosystem

💻 Technologies

Python
MLflow
FastAPI
Docker
Kubernetes
Feast
Evidently AI
GitHub Actions
Scikit-Learn
MLOps

🎯 After this video you will understand:

• How ML models are deployed in production
• How companies monitor model performance
• How CI/CD works for Machine Learning
• How feature stores prevent training-serving skew
• How to answer ML System Design interview questions
• Complete MLOps workflow used by modern companies

🔥 Perfect for:
Machine Learning Engineers
Data Scientists
MLOps Engineers
AI Engineers
Software Engineers
Interview Preparation

#MLOps #MLSystemDesign #MachineLearning #MLflow #FastAPI #Docker #Kubernetes #DataScience #ArtificialIntelligence #MLEngineering #FeatureStore #ModelDeployment #CI_CD #Python

Видео Day 19-ML System Design & MLOps | MLflow, FastAPI, Docker, CI/CD, Monitoring & Deployment канала AI & ML META